def test_simulation_3_peaks(self):
"""
Test calibration from simulation, down to 1% error . Peaks (200) and (220) from monoclinic VO2 are used,
"""
s = np.linspace(0.11, 0.8, 1024)
q = 4 * np.pi * s
c = Crystal.from_database("vo2-m1")
I = powdersim(c, s)
peak1 = (2, 0, 0)
Gx1, Gy1, Gz1 = c.scattering_vector(peak1)
q1 = np.sqrt(Gx1 ** 2 + Gy1 ** 2 + Gz1 ** 2)
arr_index1 = np.argmin(np.abs(q - q1))
peak2 = (2, 2, 0)
Gx2, Gy2, Gz2 = c.scattering_vector(peak2)
q2 = np.sqrt(Gx2 ** 2 + Gy2 ** 2 + Gz2 ** 2)
arr_index2 = np.argmin(np.abs(q - q2))
peak3 = (3, 0, -2)
Gx2, Gy2, Gz2 = c.scattering_vector(peak3)
q3 = np.sqrt(Gx2 ** 2 + Gy2 ** 2 + Gz2 ** 2)
arr_index3 = np.argmin(np.abs(q - q3))
calibrated = powder_calq(
I,
c,
peak_indices=(arr_index1, arr_index2, arr_index3),
miller_indices=(peak1, peak2, peak3),
)
self.assertTupleEqual(I.shape, calibrated.shape)
self.assertTrue(np.allclose(q, calibrated, rtol=0.01))
def powder_calq(self, crystal, peak_indices, miller_indices):
"""
Determine the scattering vector q corresponding to a polycrystalline diffraction pattern
and a known crystal structure.
For best results, multiple peaks (and corresponding Miller indices) should be provided; the
absolute minimum is two.
Parameters
----------
crystal : skued.Crystal instance
Crystal that gave rise to the diffraction data.
peak_indices : n-tuple of ints
Array index location of diffraction peaks. For best
results, peaks should be well-separated. More than two peaks can be used.
miller_indices : iterable of 3-tuples
Indices associated with the peaks of ``peak_indices``. More than two peaks can be used.
E.g. ``indices = [(2,2,0), (-3,0,2)]``
Raises
------
ValueError : if the number of peak indices does not match the number of Miller indices.
ValueError : if the number of peaks given is lower than two.
"""
I = self.powder_eq()
q = powder_calq(
I=I,
crystal=crystal,
peak_indices=peak_indices,
miller_indices=miller_indices,
)
self.powder_group["scattering_vector"].resize(I.shape)
self.powder_group["scattering_vector"].write_direct(q)